Event Detection in Football using Graph Convolutional Networks 3.1

Author: Aditya Sangram Singh Rana

Date & time: 22/09/2021 – 8:00 h

Session name: Camera Events

Supervisors: Francesc Moreno-Noguer /Antonio Rubio Romano

Abstract:

The massive growth of data collection in sports has opened numerous avenues for professional teams and media houses to gain insights from this data. The data collected includes per frame player and ball trajectories, and event annotations such as passes, fouls, cards, goals etc. Graph Convolutional Networks (GCNs) have recently been employed to process this highly unstructured tracking data which can be otherwise difficult to model because of lack of clarity on how to order players in a sequence and how to handle missing objects of interest. In this thesis, we focus on the goal of automatic event detection from football videos. We show how to model the players and the ball in each frame of the video sequence as a graph, and present the results for different types of graph convolutional layers and losses that can be used to model the temporal context present around each action.

Committee:

– President: Alejandro Cartas(UPF)

– Secretary: Javier Ruiz Hidalgo(UPC)

– Vocal: Antonio Agudo(UPF)